Methods and devices using swallowing accelerometry signals for swallowing impairment detection

ABSTRACT

A method classifies vibrational data acquired for a swallowing event to identify a possible swallowing impairment in a candidate. The method includes receiving axis-specific vibrational data for an anterior-posterior (A-P) axis and a superior-inferior (S-I) axis and representative of the swallowing event, for example from a sensor operatively coupled to a processing module that is a local or remote computing device. A portion of the axis-specific vibrational data for the A-P axis can be combined with a portion of the axis-specific vibrational data for the S-I axis on the processing module using one or more of linear combination, squared (power) sum, moving window correlation of the two signals, local minimum or local maximum of the two signals, and trigonometric relation. The method can include outputting from the processing module a classification of the swallowing event based on the combined vibrational data.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a National Stage of International ApplicationNo. PCT/EP2018/054747, filed on Feb. 27, 2018, which claims priority toU.S. Provisional Patent Application No. 62/464,849, filed on Feb. 28,2017, the entire contents of which are being incorporated herein byreference.

BACKGROUND

The present disclosure generally relates to methods and devices forswallowing impairment detection, whereby a candidate executes one ormore swallowing events and dual axis accelerometry data is acquiredrepresentative thereof. More specifically, the present disclosurerelates to methods and devices that use an anterior-posterior (A-P)signal together with a superior-inferior (S-I) signal, preferably bycombining the two signals by at least one process selected from thegroup consisting of linear combination of the two signals, squared(power) sum of the two signals, moving window correlation of the twosignals, local minimum or local maximum of the two signals, andtrigonometric relation between the two signals.

Dysphagia is characterized by impaired involuntary motor control ofswallowing process and can cause “penetration” which is the entry offoreign material into the airway. The airway invasion can be accompaniedby “aspiration” in which the foreign material enters the lungs and canlead to serious health risks.

The three phases of swallowing activity are oral, pharyngeal andesophageal. The pharyngeal phase is typically compromised in patientswith dysphagia. The impaired pharyngeal phase of swallowing in dysphagiais a prevalent health condition (38% of the population above 65 years)and may result in prandial aspiration (entry of food into the airway)and/or pharyngeal residues, which in turn can pose serious health riskssuch as aspiration pneumonia, malnutrition, dehydration, and even death.Swallowing aspiration can be silent (i.e., without any overt signs ofswallowing difficulty such as cough), especially in children withdysphagia and patients with acute stroke, rendering detection viaclinical perceptual judgement difficult.

The current gold standard for tracking swallowing activities isvideofluoroscopy that enables clinicians to monitor barium-infusedfoodstuff during swallowing via moving x-ray images. However, thevideofluoroscopy swallowing study (VFSS) cannot be done routinely due tothe expensive procedure and the need for specialized personnel, as wellas the excessive amount of harmful radiations. Another invasiveassessment is the flexible endoscopic evaluation of swallowing, whichalso requires trained personnel and entails an expensive procedure.Non-invasive alternatives for swallow monitoring include surfaceelectromyography, pulse oximetry, cervical auscultation (listening tothe breath sounds near the larynx) and swallowing accelerometry.

Despite introduction of different non-invasive approaches, a reliablebedside detection of swallowing abnormalities remains a challengingtask. For example, a recent systematic review of cervical auscultationstudies suggests that the reliability of the approach is insufficientand it can not be used as a stand-alone instrument to diagnosedysphagia. Lagarde, Marloes L J and Kamalski, Digna M A and van denEngel-Hoek, Lenie, “The reliability and validity of cervicalauscultation in the diagnosis of dysphagia: A systematic review,”Clinical Rehabilitation 30(2):1-9 (March 2015). Furthermore, perceptualclinical screening of dysphagia has been shown to lack agreement betweendifferent speech-language pathologists, possibly due to the subjectivenature of the judgement as well as the presence of variety ofenvironmental artifacts.

Over the past two decades, researchers have reported on variousswallowing screening tools, among which those driven by swallowingsounds are the most popular. Swallowing sounds are either capturedacoustically using a microphone or mechanically using an accelerometerplaced on the patient's neck measuring cervical epidermal vibrations.Reports on discriminative analysis of swallowing auscultation signalsvary in terms of the screening tool used, target swallowing problem(aspiration, penetration, pharyngeal residue), sample size, patientpopulation and medical conditions, and validation approach, which makesa direct comparison between these studies virtually impossible.

Swallowing accelerometry harnesses the hyoid and laryngeal movementsduring swallowing activities, which are manifested as epidermalvibrations measurable at the neck by an accelerometer. Vibrations inboth the anterior-posterior (A-P) and superior-inferior (S-I) anatomicaldirections are found to contain distinct information about theunderlying swallowing activities.

Nevertheless, the development of a fully automated, accurate swallowingscreening tool remains an elusive challenge.

SUMMARY

In a general embodiment, the present disclosure provides a device foridentifying a possible swallowing impairment in a candidate duringexecution of a swallowing event. The device comprises: an accelerometerconfigured to acquire axis-specific vibrational data along ananterior-posterior (A-P) axis and a superior-inferior (S-I) axis of thecandidate's throat, the axis-specific vibrational data is representativeof the swallowing event; and a processing module that is a local orremote computing device operatively coupled to the accelerometer, theprocessing module configured for processing the axis-specific data to(i) combine at least a portion of a first signal comprising theaxis-specific vibrational data acquired along the A-P axis with at leasta portion of a second signal comprising the axis-specific vibrationaldata acquired along the S-I axis using at least one process selectedfrom the group consisting of linear combination, squared (power) sum,moving window correlation of the two signals, local minimum or localmaximum of the two signals, and trigonometric relation and (ii) classifythe swallowing event as one of a plurality of classifications based onthe combined vibrational data, the plurality of classificationscomprising a first classification indicative of normal swallowing and asecond classification indicative of possibly impaired swallowing.

In an embodiment, the processing module is configured to extractmeta-features from the combined vibrational data. The processing modulecan be configured to use the meta-features extracted from the combinedvibrational data to classify the swallowing event, for example bycomparing the meta-features extracted from the combined vibrational datato preset classification criteria to classify the swallowing event. Thepreset classification criteria can be defined for each of swallowingsafety and swallowing efficiency and/or defined by features previouslyextracted and classified from a known training data set.

In an embodiment, the second classification is indicative of at leastone of a swallowing safety impairment or a swallowing efficiencyimpairment.

In an embodiment, the second classification is indicative of at leastone of penetration or aspiration, and the processing module isconfigured to further classify the swallowing event as a first eventindicative of a safe event or a second event indicative of an unsafeevent.

In an embodiment, the processing module is configured to classifymultiple successive swallowing events by classifying the combinedvibrational data for each of the successive swallowing events asindicative of one of the first classification or the secondclassification.

In an embodiment, the processing module uses a non-segmented spectrogramfor pre-processing of at least one of (i) the axis-specific vibrationaldata along the A-P axis, (ii) the axis-specific vibrational data alongthe S-I axis or (iii) the combined vibrational data.

In another general embodiment, the present disclosure provides a methodfor classifying cervical accelerometry data acquired for a swallowingevent to identify a possible swallowing impairment in a candidate. Themethod comprises: receiving axis-specific vibrational data for ananterior-posterior (A-P) axis and a superior-inferior (S-I) axis andrepresentative of the swallowing event, a processing module that is alocal or remote computing device operatively coupled to an accelerometerreceives the axis-specific vibrational data from the accelerometer;combining at least a portion of a first signal comprising theaxis-specific vibrational data for the A-P axis with at least a portionof a second signal comprising the axis-specific vibrational data for theS-I axis using at least one process selected from the group consistingof linear combination, squared (power) sum, moving window correlation ofthe two signals, local minimum or local maximum of the two signals, andtrigonometric relation, the processing module forms the combinedvibrational data; and outputting a classification of the swallowingevent as one of a plurality of classifications based on the combinedvibrational data, the plurality of classifications comprising a firstclassification indicative of normal swallowing and a secondclassification indicative of possibly impaired swallowing, and theprocessing module outputs the classification.

In an embodiment, the method comprises extracting meta-features from thecombined vibrational data on the processing module. The method cancomprise using the meta-features extracted from the combined vibrationaldata to classify the swallowing event on the processing module, forexample by comparing the meta-features extracted from the combinedvibrational data to preset classification criteria to classify theswallowing event on the processing module. The preset classificationcriteria can be defined for each of swallowing safety and swallowingefficiency and/or defined by features previously extracted andclassified from a known training data set.

In an embodiment, the second classification is indicative of at leastone of a swallowing safety impairment or a swallowing efficiencyimpairment.

In an embodiment, the second classification is indicative of at leastone of penetration or aspiration, and the method comprises furtherclassifying the swallowing event as a first event indicative of a safeevent or a second event indicative of an unsafe event.

In an embodiment, the method comprises classifying successive swallowingevents by classifying the combined vibrational data for each of thesuccessive swallowing events as indicative of one of the firstclassification or the second classification.

In an embodiment, the method comprises using a non-segmented spectrogramfor pre-processing of at least one of (i) the axis-specific vibrationaldata along the A-P axis and the S-I axis or (ii) the combinedvibrational data.

In another general embodiment, the present disclosure provides a methodof treating dysphagia in a patient. The method comprises: positioning asensor externally on the throat of the patient, the sensor acquiringvibrational data representing swallowing activity and associated with ananterior-posterior axis and a superior-inferior axis of the throat, thesensor operatively connected to a processing module configured to (i)combine at least a portion of a first signal comprising the vibrationaldata associated with the A-P axis with at least a portion of a secondsignal comprising the vibrational data associated with the S-I axisusing at least one process selected from the group consisting of linearcombination, squared (power) sum, moving window correlation of the twosignals, local minimum or local maximum of the two signals, andtrigonometric relation and (ii) classify the swallowing event as one ofa plurality of classifications based on the combined vibrational data,the plurality of classifications comprising a first classificationindicative of normal swallowing and a second classification indicativeof possibly impaired swallowing; and adjusting a feeding administered tothe patient based on the classification.

In an embodiment, the adjusting of the feeding is selected from thegroup consisting of: changing a consistency of the feeding, changing atype of food in the feeding, changing a size of a portion of the feedingadministered to the patient, changing a frequency at which portions ofthe feeding are administered to the patient, and combinations thereof.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is diagram showing the axes of acceleration in theanterior-posterior and superior-inferior directions.

FIG. 2 is a schematic diagram of an embodiment of a swallowingimpairment detection device in operation.

FIG. 3 is a schematic diagram of an embodiment of a method ofdiscriminating swallowing aspiration-penetration.

DETAILED DESCRIPTION

As used in this disclosure and the appended claims, the singular forms“a,” “an” and “the” include plural referents unless the context clearlydictates otherwise. As used herein, “about” is understood to refer tonumbers in a range of numerals, for example the range of −10% to +10% ofthe referenced number, preferably −5% to +5% of the referenced number,more preferably −1% to +1% of the referenced number, most preferably−0.1% to +0.1% of the referenced number. Moreover, all numerical rangesherein should be understood to include all integers, whole or fractions,within the range.

The words “comprise,” “comprises” and “comprising” are to be interpretedinclusively rather than exclusively. Likewise, the terms “include,”“including” and “or” should all be construed to be inclusive, unlesssuch a construction is clearly prohibited from the context. A disclosureof a device “comprising” several components does not require that thecomponents are physically attached to each other in all embodiments.

Nevertheless, the devices disclosed herein may lack any element that isnot specifically disclosed. Thus, a disclosure of an embodiment usingthe term “comprising” includes a disclosure of embodiments “consistingessentially of and” consisting of the components identified. Similarly,the methods disclosed herein may lack any step that is not specificallydisclosed herein. Thus, a disclosure of an embodiment using the term“comprising” includes a disclosure of embodiments “consistingessentially of and” consisting of the steps identified.

The term “and/or” used in the context of “X and/or Y” should beinterpreted as “X,” or “Y,” or “X and Y.” Where used herein, the terms“example” and “such as,” particularly when followed by a listing ofterms, are merely exemplary and illustrative and should not be deemed tobe exclusive or comprehensive. Any embodiment disclosed herein can becombined with any other embodiment disclosed herein unless explicitlystated otherwise.

As used herein, a “bolus” is a single sip or mouthful or a food orbeverage. As used herein, “aspiration” is entry of food or drink intothe trachea (windpipe) and lungs and can occur during swallowing and/orafter swallowing (post-deglutitive aspiration). Post-deglutitiveaspiration generally occurs as a result of pharyngeal residue thatremains in the pharynx after swallowing.

An aspect of the present disclosure is a method of processing dual-axisaccelerometry signals to classify one or more swallowing events. Anon-limiting example of such a method classifies each of the one or moreswallowing events as a swallow with aspiration-penetration or a swallowwithout aspiration-penetration. Another aspect of the present disclosureis a device that implements one or more steps of the method.

In an embodiment, the method can further comprise classifying thepatient as having safe swallowing or unsafe swallowing. For example, apatient can be classified as having unsafe swallowing if the one or moreswallowing events comprise an amount or percentage ofaspiration-penetration events that exceeds a threshold. In such anembodiment, the threshold can be zero such that the presence of anyaspiration-penetration events classifies the patient as having unsafeswallowing. Of course, in other such embodiments, the threshold can begreater than zero.

In some embodiments, the method and the device can be employed in theapparatus and/or the method for detecting aspiration disclosed in U.S.Pat. No. 7,749,177 to Chau et al., the method and/or the system ofsegmentation and time duration analysis of dual-axis swallowingaccelerometry signals disclosed in U.S. Pat. No. 8,267,875 to Chau etal., the system and/or the method for detecting swallowing activitydisclosed in U.S. Pat. No. 9,138,171 to Chau et al., or the methodand/or the device for swallowing impairment detection disclosed in U.S.Patent App. Publ. No. 2014/0228714 to Chau et al., each of which isincorporated herein by reference in its entirety.

As discussed in greater detail hereafter, the device may include asensor configured to produce signals indicating swallowing activities(e.g., a dual axis accelerometer). The sensor may be positionedexternally on the neck of a human, preferably anterior to the cricoidcartilage of the neck. A variety of means may be applied to position thesensor and to hold the sensor in such position, for example double-sidedtape. Preferably the positioning of the sensor is such that the axes ofacceleration are aligned to the anterior-posterior and super-inferiordirections, as shown in FIG. 1.

FIG. 2 generally illustrates a non-limiting example of a device 100 foruse in swallowing impairment detection. The device 100 can comprise asensor 102 (e.g., a dual axis accelerometer) to be attached in a throatarea of a candidate for acquiring dual axis accelerometry data and/orsignals during swallowing, for example illustrative S-I accelerationsignal 104. Accelerometry data may include, but is not limited to,throat vibration signals acquired along the anterior-posterior axis(A-P) and/or the superior-inferior axis (S-I). As used herein, theanterior-posterior (A-P) axis and the superior-inferior (S-I) axis arerelative to the candidate's throat.

The sensor 102 can be any accelerometer known to one of skill in thisart, for example a single axis accelerometer (which can be rotated onthe patient to obtain dual-axis vibrational data) such as an EMT 25-Csingle axis accelerometer or a dual axis accelerometer such as anADXL322 or ADXL327 dual axis accelerometer, and the present disclosureis not limited to a specific embodiment of the sensor 102.

The sensor 102 does not necessarily need to be fixed exactly in A-P andS-I orientation. In this regard, the sensor 102 can merely measure intwo perpendicular directions along the sagittal plane of the subject,and the acceleration vectors in both S-I and A-P directions can beextracted from the two sensor signals.

The sensor 102 can be operatively coupled to a processing module 106configured to process the acquired data for swallowing impairmentdetection, for example aspiration-penetration detection and/or detectionof other swallowing impairments such as swallowing inefficiencies. Theprocessing module 106 can be a distinctly implemented device operativelycoupled to the sensor 102 for communication of data thereto, forexample, by one or more data communication media such as wires, cables,optical fibers, and the like and/or by one or more wireless datatransfer protocols. In some embodiments, the processing module 106 maybe implemented integrally with the sensor 102.

Generally, the processing of the dual-axis accelerometry signalscomprises combining at least a portion of the axis-specific vibrationaldata for the A-P axis with at least a portion of the axis-specificvibrational data for the S-I axis using at least one process selectedfrom the group consisting of linear combination, squared (power) sum,moving window correlation of the two signals, local minimum or localmaximum of the two signals, and trigonometric relation. The combinedaccelerometry data can be represented as meta-features, and/ormeta-features can be extracted from the combined accelerometry data(e.g., time-frequency meta-features). In a preferred embodiment, anon-segmented spectrogram can be employed in pre-processing. Then theswallowing event can be classified based on the extracted meta-features.In applying this approach, the swallowing events may be effectivelyclassified as a normal swallowing event or a potentially impairedswallowing events (e.g., unsafe and/or inefficient). Preferably theclassification is automatic such that no user input is needed for thedual-axis accelerometry signals to be processed and used forclassification of the swallow.

FIG. 3 illustrates a non-limiting embodiment of a method 500 forclassifying a swallowing event. At Step 502, dual-axis accelerometrydata for both the S-I axis and the A-P axis is acquired or provided forone or more swallowing events, for example dual-axis accelerometry datafrom the sensor 102.

At Step 504, the dual-axis accelerometry data can optionally beprocessed to condition the accelerometry data and thus facilitatefurther processing thereof. For example, the dual-axis accelerometrydata may be filtered, denoised, and/or processed for signal artifactremoval (“preprocessed data”). In an embodiment, the dual-axisaccelerometry data is subjected to an inverse filter, which may includevarious low-pass, band-pass and/or high-pass filters, followed by signalamplification. A denoising subroutine can then applied to the inversefiltered data, preferably processing signal wavelets and iterating tofind a minimum mean square error.

In an embodiment, the preprocessing may comprise a subroutine for theremoval of movement artifacts from the data, for example, in relation tohead movement by the patient. Additionally or alternatively, othersignal artifacts, such as vocalization and blood flow, may be removedfrom the dual-axis accelerometry data. Nevertheless, the method 500 isnot limited to a specific embodiment of the preprocessing of theaccelerometry data, and the preprocessing may comprise any known methodfor filtering, denoising and/or removing signal artifacts.

At Step 506, the accelerometry data (either raw or preprocessed) canthen be automatically or manually segmented into distinct swallowingevents. Preferably the accelerometry data is automatically segmented. Inan embodiment, the segmentation is automatic and energy-based.Additionally or alternatively, manual segmentation may be applied, forexample by visual inspection of the data. The method 500 is not limitedto a specific process of segmentation, and the process of segmentationcan be any segmentation process known to one skilled in this art.

At Step 508, meta-feature based representation of combined accelerometrydata is performed. In a preferred embodiment, at least a portion of theaxis-specific vibrational data for the A-P axis can be combined with atleast a portion of the axis-specific vibrational data for the S-I axisusing at least one process selected from the group consisting of linearcombination, squared (power) sum, moving window correlation of the twosignals, local minimum or local maximum of the two signals, andtrigonometric relation. This processing can occur before anysegmentation and/or after any segmentation.

In an embodiment, the dual-axis accelerometry data (e.g., bivariatebolus signals that have been preprocessed and/or normalized) isconverted to univariate signals using the windowed inner-product oftheir A-P and S-I channels with a predetermined window size (e.g., 750samples) and a predetermined amount of overlap between successivewindows (e.g., 90% overlap). The resulting univariate bolus signals(referred to as “inner-product signals” hereafter) can then berepresented in terms of meta-features. The meta-feature representationof bolus signals can then be used as the input along with respectivelabels in subsequent feature selection and/or classification.

At Step 510 (which is optional), a subset of the meta-features may beselected for classification, for example based on the previous analysisof similar extracted feature sets derived during classifier trainingand/or calibration. For example, in one embodiment, the most prominentfeatures or feature components/levels extracted from the classifiertraining data set are retained as most likely to provide classifiableresults when applied to new test data, and are thus selected to define areduced feature set for training the classifier and ultimately enablingclassification. For instance, in the context of wavelet decompositions,or other such signal decompositions, techniques such as lineardiscriminant analysis, principle component analysis or other suchtechniques effectively implemented to qualify a quantity and/or qualityof information available from a given decomposition level, may be usedon the training data set to preselect feature components or levels mostlikely to provide the highest level of usable information in classifyingnewly acquired signals. Such preselected feature components/levels canthen be used to train the classifier for subsequent classifications.Ultimately, these preselected features can be used in characterizing theclassification criteria for subsequent classifications.

Accordingly, where the device has been configured to operate from areduced feature set, such as described above, this reduced feature setcan be characterized by a predefined feature subset or feature reductioncriteria that resulted from the previous implementation of a featurereduction technique on the classifier training data set. Newly acquireddata can thus proceed through the various pre-processing andsegmentation steps described above (steps 504, 506), the variousswallowing events so identified then processed for feature extraction atstep 508 (e.g., full feature set), and those features corresponding withthe preselected subset retained at step 510 for classification at step512.

While the above exemplary approach contemplates a discrete selection ofthe most prominent features, other techniques may also readily apply.For example, in some embodiments, the results of the feature reductionprocess may rather be manifested in a weighted series or vector forassociation with the extracted feature set in assigning a particularweight or level of significance to each extracted feature component orlevel during the classification process. In particular, selection of themost prominent feature components to be used for classification can beimplemented via linear discriminant analysis (LDA) on the classifiertraining data set. Consequently, feature extraction and reduction can beeffectively used to distinguish safe swallows from potentially unsafeswallows, and efficient swallows from potentially inefficient swallows.In this regard, the extraction of the selected features from new testdata can be compared to preset classification criteria established as afunction of these same selected features as previously extracted andreduced from an adequate training data set, to classify the new testdata as representative of a normal vs. impaired swallow (e.g., safeswallows vs. unsafe swallows, and/or efficient swallows vs. inefficientswallows). As will be appreciated by the skilled artisan, other featuresets such as frequency, time and/or time-frequency domain features maybe used.

At Step 512, feature classification can be implemented. Extractedfeatures (or a reduced/weighted subset thereof) of acquiredswallow-specific data can be compared with preset classificationcriteria to classify each data set as representative of a normalswallowing event or a potentially impaired swallowing event.

In an embodiment, the method 500 can optionally comprise atraining/validation subroutine Step 516 in which a data setrepresentative of multiple swallows is processed such that eachswallow-specific data set ultimately experiences the preprocessing,feature extraction and feature reduction disclosed herein. A validationloop can be applied to the discriminant analysis-based classifier usinga cross-validation test. After all events have been classified andvalidated, output criteria may be generated for future classificationwithout necessarily applying further validation to the classificationcriteria. Alternatively, routine validation may be implemented to eitherrefine the statistical significance of classification criteria, or againas a measure to accommodate specific equipment and/or protocol changes(e.g. recalibration of specific equipment, for example, upon replacingthe accelerometer with same or different accelerometer type/model,changing operating conditions, new processing modules such as furtherpreprocessing subroutines, artifact removal, additional featureextraction/reduction, etc.).

The classification can be used to determine and output which swallowingevent represented a normal swallowing event as compared to apenetration, an aspiration, a swallowing safety impairment and/or answallowing efficiency impairment at Step 514. In some embodiments, theswallowing event can be further classified as a safe event or an unsafeevent.

For example, the processing module 106 and/or a device associated withthe processing module 106 can comprise a display that identifies aswallow or an aspiration using images such as text, icons, colors,lights turned on and off, and the like. Alternatively or additionally,the processing module 106 and/or a device associated with the processingmodule 106 can comprise a speaker that identifies a swallow or anaspiration using auditory signals. The present disclosure is not limitedto a specific embodiment of the output, and the output can be any meansby which the classification of the swallowing event is identified to auser of the device 100, such as a clinician or a patient.

The output may then be utilized in screening/diagnosing the testedcandidate and providing appropriate treatment, further testing, and/orproposed dietary or other related restrictions thereto until furtherassessment and/or treatment may be applied. For example, adjustments tofeedings can be based on changing consistency or type of food and/or thesize and/or frequency of mouthfuls being offered to the patient.

Alternative types of vibration sensors other than accelerometers can beused with appropriate modifications to be the sensor 102. For example, asensor can measure displacement (e.g., a microphone), while theprocessing module 106 records displacement signals over time. As anotherexample, a sensor can measure velocity, while the processing module 106records velocity signals over time. Such signals can then be convertedinto acceleration signals and processed as disclosed herein and/or byother techniques of feature extraction and classification appropriatefor the type of received signal.

Another aspect of the present disclosure is a method of treatingdysphagia. The term “treat” includes both prophylactic or preventivetreatment (that prevent and/or slow the development of dysphagia) andcurative, therapeutic or disease-modifying treatment, includingtherapeutic measures that cure, slow down, lessen symptoms of, and/orhalt progression of dysphagia; and treatment of patients at risk ofdysphagia, for example patients having another disease or medicalcondition that increase their risk of dysphagia relative to a healthyindividual of similar characteristics (age, gender, geographic location,and the like). The term does not necessarily imply that a subject istreated until total recovery. The term “treat” also refers to themaintenance and/or promotion of health in an individual not sufferingfrom dysphagia but who may be susceptible to the development ofdysphagia. The term “treat” also includes the potentiation or otherwiseenhancement of one or more primary prophylactic or therapeutic measures.The term “treat” further includes the dietary management of dysphagia orthe dietary management for prophylaxis or prevention of dysphagia. Atreatment can be conducted by a patient, a clinician and/or any otherindividual or entity.

The method of treating dysphagia comprises using any embodiment of thedevice 100 disclosed herein and/or performing any embodiment of themethod 500 disclosed herein. The method can further comprise adjusting afeeding administered to the patient based on the classification, forexample by changing a consistency of the feeding, changing a type offood in the feeding, changing a size of a portion of the feedingadministered to the patient, changing a frequency at which portions ofthe feeding are administered to the patient, or combinations thereof.

In an embodiment, the method prevents aspiration pneumonia fromdysphagia. In an embodiment, the dysphagia is oral pharyngeal dysphagiaassociated with a condition selected from the group consisting ofcancer, cancer chemotherapy, cancer radiotherapy, surgery for oralcancer, surgery for throat cancer, a stroke, a brain injury, aprogressive neuromuscular disease, neurodegenerative diseases, anelderly age of the patient, and combinations thereof. As used herein, an“elderly” human is a person with a chronological age of 65 years orolder.

It should be understood that various changes and modifications to thepresently preferred embodiments described herein will be apparent tothose skilled in the art. Such changes and modifications can be madewithout departing from the spirit and scope of the present subjectmatter and without diminishing its intended advantages. It is thereforeintended that such changes and modifications be covered by the appendedclaims.

The invention is claimed as follows:
 1. A method of treating dysphagiain a patient, the method comprising: positioning a sensor externally onthe throat of the patient, the sensor acquiring vibrational datarepresenting swallowing activity and associated with ananterior-posterior (A-P) axis and a superior-inferior (S-I) axis of thethroat, the sensor operatively connected to a processing moduleconfigured to (i) combine at least a portion of a first signalcomprising the vibrational data associated with the A-P axis with atleast a portion of a second signal comprising the vibrational dataassociated with the S-I axis using at least one process selected fromthe group consisting of moving window correlation of the two signals,local minimum or local maximum of the two signals, and trigonometricrelation to form combined vibrational data and (ii) classify theswallowing event as one of a plurality of classifications based on thecombined vibrational data, the plurality of classifications comprising afirst classification indicative of normal swallowing and a secondclassification indicative of possibly impaired swallowing; and adjustinga feeding administered to the patient based on the classification. 2.The method of claim 1 wherein the adjusting of the feeding is selectedfrom the group consisting of: changing a consistency of the feeding,changing a type of food in the feeding, changing a size of a portion ofthe feeding administered to the patient, changing a frequency at whichportions of the feeding are administered to the patient, andcombinations thereof.
 3. The method of claim 1 wherein the sensormeasures two sensor signals in perpendicular directions along a sagittalplane of the patient, and the vibrational data associated with the A-Paxis and the vibrational data associated with the S-I axis are extractedfrom the two sensor signals.
 4. The method of claim 1 wherein theprocessing module is configured to extract meta-features from thecombined vibrational data.
 5. The method of claim 4 wherein theprocessing module is configured to use the meta-features extracted fromthe combined vibrational data to classify the swallowing event.
 6. Themethod of claim 5 wherein the processing module is configured to comparethe meta-features extracted from the combined vibrational data to presetclassification criteria to classify the swallowing event.
 7. The methodof claim 1 wherein the second classification is indicative of at leastone of a swallowing safety impairment or a swallowing efficiencyimpairment.
 8. The method of claim 1 wherein the second classificationis indicative of at least one of penetration or aspiration, and theprocessing module is configured to further classify the swallowing eventas a first event indicative of a safe event or a second event indicativeof an unsafe event.
 9. The method of claim 1 wherein the processingmodule is configured to classify multiple successive swallowing eventsby classifying the combined vibrational data for each of the successiveswallowing events as indicative of one of the first classification orthe second classification.
 10. The method of claim 1 wherein theprocessing module uses a non-segmented spectrogram for pre-processing ofat least one of (i) the axis-specific vibrational data along the A-Paxis, (ii) the axis-specific vibrational data along the S-I axis, or(iii) the combined vibrational data.
 11. The method of claim 1comprising screening and/or diagnosing the patient based on theclassification.
 12. The method of claim 1 comprising preventingaspiration pneumonia from dysphagia.